The traditional methods cannot be used to meet the requirements of rapid and objective detection of meat freshness. Electronic nose (E-Nose), computer vision (CV), and artificial tactile (AT) sensory technologies can be used to mimic humans’ compressive sensory functions of smell, look, and touch when making judgement of meat quality (freshness). Though individual E-Nose, CV, and AT sensory technologies have been used to detect the meat freshness, the detection results vary and are not reliable. In this paper, a new method has been proposed through the integration of E-Nose, CV, and AT sensory technologies for capturing comprehensive meat freshness parameters and the data fusion method for analysing the complicated data with different dimensions and units of six odour parameters of E-Nose, 9 colour parameters of CV, and 4 rubbery parameters of AT for effective meat freshness detection. The pork and chicken meats have been selected for a validation test. The total volatile base nitrogen (TVB-N) assays are used to define meat freshness as the standard criteria for validating the effectiveness of the proposed method. The principal component analysis (PCA) and support vector machine (SVM) are used as unsupervised and supervised pattern recognition methods to analyse the source data and the fusion data of the three instruments, respectively. The experimental and data analysis results show that compared to a single technology, the fusion of E-Nose, CV, and AT technologies significantly improves the detection performance of various freshness meat products. In addition, partial least squares (PLS) is used to construct TVB-N value prediction models, in which the fusion data is input. The root mean square error predictions (RMSEP) for the sample pork and chicken meats are 1.21 and 0.98, respectively, in which the coefficient of determination (R2) is 0.91 and 0.94. This means that the proposed method can be used to effectively detect meat freshness and the storage time (days).

Ripening agents can accelerate the ripening of fruits and maintain a similar appearance to naturally ripe fruits, but the fruit flavor and quality will be changed compared to naturally ripe fruits. To find an efficient detection method to distinguish whether crab apples were artificial ripened, the naturally ripe and artificially ripe fruits were detected and analyzed using the electronic nose (e-nose) technique in this study. The fruit quality indexes of samples were determined by the traditional method as a reference. Significant differences were found between naturally ripe and artificially ripe fruits based on the analysis of soluble sugar content, titratable acidity content, sugar–acid ratio, soluble protein content, and soluble solids content. In addition, principal component analysis (PCA), linear discriminant analysis (LDA), support vector machine (SVM), and random forest (RF) analyses were performed on the electrical signals generated by the electronic nose sensor, respectively. The results showed that the RF is the best recognition algorithm for distinguishing which crab apples were naturally ripe or artificially ripe; the average recognition accuracy is 98.3%. On the other hand, the prediction models between the e-nose response data and fruit quality indexes were constructed by partial least squares regression (PLSR), which showed that the feature value of e-nose response curves extracted by wavelet transform was highly correlated with the quality indexes of fruits, the determination coefficients (R²) of regression models were higher than 0.91. The results demonstrated that the detection technology with an electronic nose could be used to test whether the fruit of the crab apple was artificially ripe, which is an economical and efficient method.